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FVC: An End-to-End Framework Towards Deep Video Compression in Feature Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 29, 2022
Summary
This study introduces a novel feature-space video coding (FVC) framework for enhanced deep video compression. The FVC framework improves motion estimation and compensation, achieving state-of-the-art results on benchmark datasets.
Area of Science:
- Computer Vision
- Machine Learning
- Video Compression
Background:
- Deep learning is advancing video compression, but current hybrid methods struggle with accurate motion estimation and compensation in pixel space.
- Existing learning-based video compression techniques often face limitations in handling spatial and temporal redundancies effectively.
Purpose of the Study:
- To propose a novel feature-space video coding (FVC) framework that performs key operations in the feature space for improved video compression.
- To introduce a deformable compensation module for more effective motion compensation and residual compression.
- To develop resolution-adaptive modules for enhanced motion and residual coding.
Main Methods:
- Developed a feature-space video coding (FVC) framework executing motion estimation, compression, compensation, and residual compression in the feature space.
- Introduced a deformable compensation module utilizing feature-space motion estimation, auto-encoder based motion information compression, and deformable convolutions for prediction.
- Proposed resolution-adaptive motion coding (RaMC) and resolution-adaptive residual coding (RaRC) modules to handle varying motion and residual patterns.
Main Results:
- The proposed FVC framework demonstrated state-of-the-art performance on HEVC, UVG, and MCL-JCV benchmark datasets.
- The deformable compensation module significantly improved motion compensation effectiveness.
- Resolution-adaptive modules enhanced the framework's ability to process diverse motion and residual characteristics.
Conclusions:
- The feature-space video coding framework offers a significant advancement over traditional pixel-space methods for deep video compression.
- The proposed deformable compensation and resolution-adaptive modules are key innovations leading to superior compression performance.
- This approach sets a new benchmark for learning-based video compression techniques.
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